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1.8 KiB
1.8 KiB
The exercice is validated is all questions of the exercice are validated
The question 1 is validated if the proportion of class Benign is 0.6552217453505007. It means that if you always predict Benign your accuracy would be 66%.
The question 2 is validated if the proportion of one of the classes is the approximately the same on the train and test set: ~0.65. In my case:
- test: 0.6571428571428571
- train: 0.6547406082289803
The question 3 is validated if the output is:
# Train
Class prediction on train set:
[4 2 4 2 2 2 2 4 2 2]
Probability prediction on train set:
[0.99600415 0.00908666 0.99992744 0.00528803 0.02097154 0.00582772
0.03565076 0.99515326 0.00788281 0.01065484]
Score on train set:
0.9695885509838998
#Test
Class prediction on test set:
[2 2 2 4 2 4 2 2 2 4]
Probability prediction on test set:
[0.01747203 0.22495309 0.00698756 0.54020801 0.0015289 0.99862249
0.33607994 0.01227679 0.00438157 0.99972344]
Score on test set:
0.9642857142857143
Only the 10 first predictions are outputted. The score is computed on all the data in the folds. For some reasons, you may have a different data splitting as mine. The requirement for this question is to have a score on the test set bigger than 92%.
If the score is 1, congratulate you peer, he's just leaked his first target. The target should be dropped from the X_train or X_test ;) !
The question 4 is validated if the confusion matrix on the train set is similar to:
array([[357, 9],
[ 8, 185]])
and if the confusion matrix on the test set is similar to:
array([[90, 2],
[ 3, 45]])
As said, for some reasons, the results may be slightly different from mine because of the data splitting. However, the values in the confusion matrix should be close to these results.